Overview

Dataset statistics

Number of variables36
Number of observations135038
Missing cells293365
Missing cells (%)6.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.1 MiB
Average record size in memory288.0 B

Variable types

Numeric23
Categorical13

Alerts

Product_type has constant value "FRM" Constant
Seller_Name has a high cardinality: 94 distinct values High cardinality
Property_state has a high cardinality: 54 distinct values High cardinality
Origination_Date has a high cardinality: 234 distinct values High cardinality
First_payment_date has a high cardinality: 235 distinct values High cardinality
Qdate has a high cardinality: 78 distinct values High cardinality
Original_Interest_Rate is highly correlated with CPIAUCSL and 4 other fieldsHigh correlation
Original_LTV_(OLTV) is highly correlated with Original_Combined_LTV_(CLTV) and 1 other fieldsHigh correlation
Original_Combined_LTV_(CLTV) is highly correlated with Original_LTV_(OLTV) and 1 other fieldsHigh correlation
Borrower_Credit_Score_at_Origination is highly correlated with Co-borrower_credit_score_at_originationHigh correlation
Primary_mortgage_insurance_percent is highly correlated with Original_LTV_(OLTV) and 1 other fieldsHigh correlation
Co-borrower_credit_score_at_origination is highly correlated with Borrower_Credit_Score_at_OriginationHigh correlation
UNRATE is highly correlated with DEXUSEUHigh correlation
CPIAUCSL is highly correlated with Original_Interest_Rate and 7 other fieldsHigh correlation
TCMR is highly correlated with Original_Interest_Rate and 4 other fieldsHigh correlation
POILWTIUSDM is highly correlated with Original_Interest_Rate and 7 other fieldsHigh correlation
TTLCONS is highly correlated with CPIAUCSL and 2 other fieldsHigh correlation
DEXUSEU is highly correlated with UNRATE and 3 other fieldsHigh correlation
BOPGSTB is highly correlated with POILWTIUSDM and 2 other fieldsHigh correlation
GOLDAMGBD228NLBM is highly correlated with Original_Interest_Rate and 6 other fieldsHigh correlation
CSUSHPISA is highly correlated with CPIAUCSL and 5 other fieldsHigh correlation
MSPUS is highly correlated with Original_Interest_Rate and 7 other fieldsHigh correlation
Original_Interest_Rate is highly correlated with CPIAUCSL and 4 other fieldsHigh correlation
Original_LTV_(OLTV) is highly correlated with Original_Combined_LTV_(CLTV) and 1 other fieldsHigh correlation
Original_Combined_LTV_(CLTV) is highly correlated with Original_LTV_(OLTV) and 1 other fieldsHigh correlation
Borrower_Credit_Score_at_Origination is highly correlated with Co-borrower_credit_score_at_originationHigh correlation
Primary_mortgage_insurance_percent is highly correlated with Original_LTV_(OLTV) and 1 other fieldsHigh correlation
Co-borrower_credit_score_at_origination is highly correlated with Borrower_Credit_Score_at_OriginationHigh correlation
UNRATE is highly correlated with TTLCONS and 1 other fieldsHigh correlation
CPIAUCSL is highly correlated with Original_Interest_Rate and 7 other fieldsHigh correlation
TCMR is highly correlated with Original_Interest_Rate and 5 other fieldsHigh correlation
POILWTIUSDM is highly correlated with CPIAUCSL and 3 other fieldsHigh correlation
TTLCONS is highly correlated with UNRATE and 4 other fieldsHigh correlation
DEXUSEU is highly correlated with UNRATE and 4 other fieldsHigh correlation
BOPGSTB is highly correlated with TTLCONS and 2 other fieldsHigh correlation
GOLDAMGBD228NLBM is highly correlated with Original_Interest_Rate and 6 other fieldsHigh correlation
CSUSHPISA is highly correlated with Original_Interest_Rate and 6 other fieldsHigh correlation
MSPUS is highly correlated with Original_Interest_Rate and 5 other fieldsHigh correlation
Original_Interest_Rate is highly correlated with CPIAUCSL and 3 other fieldsHigh correlation
Original_LTV_(OLTV) is highly correlated with Original_Combined_LTV_(CLTV) and 1 other fieldsHigh correlation
Original_Combined_LTV_(CLTV) is highly correlated with Original_LTV_(OLTV) and 1 other fieldsHigh correlation
Borrower_Credit_Score_at_Origination is highly correlated with Co-borrower_credit_score_at_originationHigh correlation
Primary_mortgage_insurance_percent is highly correlated with Original_LTV_(OLTV) and 1 other fieldsHigh correlation
Co-borrower_credit_score_at_origination is highly correlated with Borrower_Credit_Score_at_OriginationHigh correlation
CPIAUCSL is highly correlated with Original_Interest_Rate and 5 other fieldsHigh correlation
TCMR is highly correlated with Original_Interest_Rate and 3 other fieldsHigh correlation
POILWTIUSDM is highly correlated with CPIAUCSL and 2 other fieldsHigh correlation
TTLCONS is highly correlated with CSUSHPISA and 1 other fieldsHigh correlation
DEXUSEU is highly correlated with POILWTIUSDM and 1 other fieldsHigh correlation
GOLDAMGBD228NLBM is highly correlated with Original_Interest_Rate and 5 other fieldsHigh correlation
CSUSHPISA is highly correlated with CPIAUCSL and 2 other fieldsHigh correlation
MSPUS is highly correlated with Original_Interest_Rate and 5 other fieldsHigh correlation
Product_type is highly correlated with Property_state and 9 other fieldsHigh correlation
Property_state is highly correlated with Product_typeHigh correlation
Qdate is highly correlated with Product_typeHigh correlation
Origination_Channel is highly correlated with Product_typeHigh correlation
Number_of_units is highly correlated with Product_typeHigh correlation
Property_type is highly correlated with Product_typeHigh correlation
Seller_Name is highly correlated with Product_typeHigh correlation
Loan_purpose is highly correlated with Product_typeHigh correlation
Occupancy_type is highly correlated with Product_typeHigh correlation
First_time_home_buyer_indicator is highly correlated with Product_typeHigh correlation
Mortgage_Insurance_type is highly correlated with Product_typeHigh correlation
Origination_Channel is highly correlated with Seller_NameHigh correlation
Seller_Name is highly correlated with Origination_Channel and 11 other fieldsHigh correlation
Original_Interest_Rate is highly correlated with Seller_Name and 11 other fieldsHigh correlation
Original_Loan_Term is highly correlated with Primary_mortgage_insurance_percentHigh correlation
Original_LTV_(OLTV) is highly correlated with Original_Combined_LTV_(CLTV) and 1 other fieldsHigh correlation
Original_Combined_LTV_(CLTV) is highly correlated with Original_LTV_(OLTV) and 1 other fieldsHigh correlation
Borrower_Credit_Score_at_Origination is highly correlated with Co-borrower_credit_score_at_originationHigh correlation
Property_state is highly correlated with Zip_code_shortHigh correlation
Zip_code_short is highly correlated with Property_stateHigh correlation
Primary_mortgage_insurance_percent is highly correlated with Original_Loan_Term and 2 other fieldsHigh correlation
Co-borrower_credit_score_at_origination is highly correlated with Borrower_Credit_Score_at_OriginationHigh correlation
UNRATE is highly correlated with Seller_Name and 12 other fieldsHigh correlation
CPIAUCSL is highly correlated with Seller_Name and 12 other fieldsHigh correlation
Qdate is highly correlated with Seller_Name and 12 other fieldsHigh correlation
rGDP is highly correlated with UNRATE and 10 other fieldsHigh correlation
TCMR is highly correlated with Seller_Name and 12 other fieldsHigh correlation
POILWTIUSDM is highly correlated with Seller_Name and 12 other fieldsHigh correlation
TTLCONS is highly correlated with Seller_Name and 12 other fieldsHigh correlation
DEXUSEU is highly correlated with Seller_Name and 12 other fieldsHigh correlation
BOPGSTB is highly correlated with Original_Interest_Rate and 11 other fieldsHigh correlation
GOLDAMGBD228NLBM is highly correlated with Seller_Name and 12 other fieldsHigh correlation
CSUSHPISA is highly correlated with Seller_Name and 12 other fieldsHigh correlation
MSPUS is highly correlated with Seller_Name and 12 other fieldsHigh correlation
Original_Debt_to_Income_Ratio has 2642 (2.0%) missing values Missing
Primary_mortgage_insurance_percent has 110716 (82.0%) missing values Missing
Co-borrower_credit_score_at_origination has 67672 (50.1%) missing values Missing
Mortgage_Insurance_type has 110716 (82.0%) missing values Missing

Reproduction

Analysis started2022-04-06 17:47:01.696627
Analysis finished2022-04-06 17:49:02.034217
Duration2 minutes and 0.34 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Loan_Identifier
Real number (ℝ≥0)

Distinct125363
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.518022819 × 1011
Minimum1.00002 × 1011
Maximum9.99985 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:02.162538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.00002 × 1011
5-th percentile1.4584125 × 1011
Q13.270665 × 1011
median5.525325 × 1011
Q37.7732825 × 1011
95-th percentile9.561356 × 1011
Maximum9.99985 × 1011
Range8.99983 × 1011
Interquartile range (IQR)4.5026175 × 1011

Descriptive statistics

Standard deviation2.597821488 × 1011
Coefficient of variation (CV)0.4707884642
Kurtosis-1.201513582
Mean5.518022819 × 1011
Median Absolute Deviation (MAD)2.251105 × 1011
Skewness-0.008013935302
Sum7.451427654 × 1016
Variance6.748676485 × 1022
MonotonicityNot monotonic
2022-04-06T13:49:02.308800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.9732 × 10114
 
< 0.1%
2.59153 × 10114
 
< 0.1%
7.57067 × 10114
 
< 0.1%
7.50054 × 10114
 
< 0.1%
4.3854 × 10114
 
< 0.1%
6.65386 × 10114
 
< 0.1%
1.38101 × 10114
 
< 0.1%
5.37573 × 10114
 
< 0.1%
9.98433 × 10114
 
< 0.1%
3.31339 × 10114
 
< 0.1%
Other values (125353)134998
> 99.9%
ValueCountFrequency (%)
1.00002 × 10111
 
< 0.1%
1.00032 × 10111
 
< 0.1%
1.00035 × 10111
 
< 0.1%
1.00041 × 10111
 
< 0.1%
1.00046 × 10111
 
< 0.1%
1.00049 × 10111
 
< 0.1%
1.00051 × 10111
 
< 0.1%
1.00061 × 10111
 
< 0.1%
1.00069 × 10111
 
< 0.1%
1.00072 × 10113
< 0.1%
ValueCountFrequency (%)
9.99985 × 10111
< 0.1%
9.99972 × 10111
< 0.1%
9.9997 × 10111
< 0.1%
9.99965 × 10111
< 0.1%
9.99951 × 10111
< 0.1%
9.9995 × 10111
< 0.1%
9.99949 × 10111
< 0.1%
9.99947 × 10111
< 0.1%
9.99934 × 10111
< 0.1%
9.99919 × 10111
< 0.1%

Origination_Channel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
R
67857 
C
46756 
B
20425 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowC
5th rowR

Common Values

ValueCountFrequency (%)
R67857
50.3%
C46756
34.6%
B20425
 
15.1%

Length

2022-04-06T13:49:02.433766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-06T13:49:02.658568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
r67857
50.3%
c46756
34.6%
b20425
 
15.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Seller_Name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct94
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
OTHER
46014 
BANK OF AMERICA, N.A.
15626 
WELLS FARGO BANK, N.A.
14838 
JPMORGAN CHASE BANK, NA
9712 
JPMORGAN CHASE BANK, NATIONAL ASSOCIATION
7836 
Other values (89)
41012 

Length

Max length68
Median length18
Mean length17.40043543
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowOTHER
2nd rowPNC BANK, N.A.
3rd rowOTHER
4th rowAMTRUST BANK
5th rowOTHER

Common Values

ValueCountFrequency (%)
OTHER46014
34.1%
BANK OF AMERICA, N.A.15626
 
11.6%
WELLS FARGO BANK, N.A.14838
 
11.0%
JPMORGAN CHASE BANK, NA9712
 
7.2%
JPMORGAN CHASE BANK, NATIONAL ASSOCIATION7836
 
5.8%
CITIMORTGAGE, INC.5950
 
4.4%
GMAC MORTGAGE, LLC4278
 
3.2%
FLAGSTAR BANK, FSB3308
 
2.4%
SUNTRUST MORTGAGE INC.3059
 
2.3%
QUICKEN LOANS INC.3007
 
2.2%
Other values (84)21410
15.9%

Length

2022-04-06T13:49:02.753225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bank59587
15.9%
other46014
 
12.3%
n.a31896
 
8.5%
chase17971
 
4.8%
jpmorgan17548
 
4.7%
of15977
 
4.3%
america15724
 
4.2%
wells14881
 
4.0%
fargo14881
 
4.0%
mortgage14179
 
3.8%
Other values (139)126427
33.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Original_Interest_Rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct885
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.238376478
Minimum2.25
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:02.887395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.25
5-th percentile3.309849915
Q14.25
median5.25
Q36.125
95-th percentile7.375
Maximum11
Range8.75
Interquartile range (IQR)1.875

Descriptive statistics

Standard deviation1.28989546
Coefficient of variation (CV)0.2462395488
Kurtosis-0.5204123179
Mean5.238376478
Median Absolute Deviation (MAD)1
Skewness0.2567090033
Sum707379.8828
Variance1.663830299
MonotonicityNot monotonic
2022-04-06T13:49:03.024805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.8756754
 
5.0%
4.8755729
 
4.2%
4.3754937
 
3.7%
4.254926
 
3.6%
5.754820
 
3.6%
3.8754620
 
3.4%
4.54383
 
3.2%
64368
 
3.2%
6.254174
 
3.1%
5.3754146
 
3.1%
Other values (875)86181
63.8%
ValueCountFrequency (%)
2.254
 
< 0.1%
2.37517
 
< 0.1%
2.451
 
< 0.1%
2.5143
0.1%
2.541
 
< 0.1%
2.552
 
< 0.1%
2.59999993
 
< 0.1%
2.625270
0.2%
2.65000011
 
< 0.1%
2.66000011
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
10.252
 
< 0.1%
10.1251
 
< 0.1%
101
 
< 0.1%
9.8751
 
< 0.1%
9.754
 
< 0.1%
9.6259
< 0.1%
9.517
< 0.1%
9.37513
< 0.1%
9.32499981
 
< 0.1%

Original_UPB
Real number (ℝ≥0)

Distinct717
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188931.0713
Minimum8000
Maximum1170000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:03.155602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum8000
5-th percentile59000
Q1108000
median164000
Q3247000
95-th percentile405000
Maximum1170000
Range1162000
Interquartile range (IQR)139000

Descriptive statistics

Standard deviation108742.3794
Coefficient of variation (CV)0.5755664151
Kurtosis2.089248292
Mean188931.0713
Median Absolute Deviation (MAD)64000
Skewness1.241016237
Sum2.5512874 × 1010
Variance1.182490507 × 1010
MonotonicityNot monotonic
2022-04-06T13:49:03.312427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4170001986
 
1.5%
1000001972
 
1.5%
2000001714
 
1.3%
1500001514
 
1.1%
1200001263
 
0.9%
1600001153
 
0.9%
1400001108
 
0.8%
1800001068
 
0.8%
900001064
 
0.8%
800001045
 
0.8%
Other values (707)121151
89.7%
ValueCountFrequency (%)
80001
 
< 0.1%
100002
 
< 0.1%
110005
< 0.1%
130003
 
< 0.1%
140004
< 0.1%
150008
< 0.1%
160005
< 0.1%
170005
< 0.1%
180008
< 0.1%
190008
< 0.1%
ValueCountFrequency (%)
11700001
< 0.1%
11210001
< 0.1%
10000001
< 0.1%
9850001
< 0.1%
9380001
< 0.1%
9270001
< 0.1%
8700001
< 0.1%
8150002
< 0.1%
8130001
< 0.1%
8050001
< 0.1%

Original_Loan_Term
Real number (ℝ≥0)

HIGH CORRELATION

Distinct127
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.0648262
Minimum60
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:03.459088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile180
Q1240
median360
Q3360
95-th percentile360
Maximum360
Range300
Interquartile range (IQR)120

Descriptive statistics

Standard deviation82.33167429
Coefficient of variation (CV)0.2681247322
Kurtosis-0.7829963816
Mean307.0648262
Median Absolute Deviation (MAD)0
Skewness-1.011882662
Sum41465420
Variance6778.504591
MonotonicityNot monotonic
2022-04-06T13:49:03.592934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36093557
69.3%
18029335
 
21.7%
2406223
 
4.6%
1203993
 
3.0%
300986
 
0.7%
14472
 
0.1%
9659
 
< 0.1%
15656
 
< 0.1%
34849
 
< 0.1%
16849
 
< 0.1%
Other values (117)659
 
0.5%
ValueCountFrequency (%)
604
 
< 0.1%
722
 
< 0.1%
781
 
< 0.1%
848
 
< 0.1%
9659
< 0.1%
1001
 
< 0.1%
1031
 
< 0.1%
1061
 
< 0.1%
10819
 
< 0.1%
1091
 
< 0.1%
ValueCountFrequency (%)
36093557
69.3%
3594
 
< 0.1%
3584
 
< 0.1%
3575
 
< 0.1%
35612
 
< 0.1%
35515
 
< 0.1%
35427
 
< 0.1%
35329
 
< 0.1%
35221
 
< 0.1%
35123
 
< 0.1%

Original_LTV_(OLTV)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct94
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.05729498
Minimum4
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:03.735477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile35
Q160
median75
Q380
95-th percentile95
Maximum97
Range93
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.4931782
Coefficient of variation (CV)0.2496981679
Kurtosis0.2871075184
Mean70.05729498
Median Absolute Deviation (MAD)9
Skewness-0.8332872297
Sum9460397
Variance306.0112836
MonotonicityNot monotonic
2022-04-06T13:49:03.879836image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8027633
20.5%
958194
 
6.1%
756801
 
5.0%
906241
 
4.6%
703848
 
2.8%
793484
 
2.6%
782864
 
2.1%
742689
 
2.0%
732525
 
1.9%
772379
 
1.8%
Other values (84)68380
50.6%
ValueCountFrequency (%)
41
 
< 0.1%
54
 
< 0.1%
65
 
< 0.1%
715
 
< 0.1%
812
 
< 0.1%
922
 
< 0.1%
1032
< 0.1%
1141
< 0.1%
1252
< 0.1%
1365
< 0.1%
ValueCountFrequency (%)
971961
 
1.5%
9662
 
< 0.1%
958194
6.1%
94613
 
0.5%
93533
 
0.4%
92486
 
0.4%
91248
 
0.2%
906241
4.6%
89895
 
0.7%
88749
 
0.6%

Original_Combined_LTV_(CLTV)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct110
Distinct (%)0.1%
Missing1031
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean70.86085801
Minimum4
Maximum142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:04.022003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile36
Q161
median75
Q380
95-th percentile95
Maximum142
Range138
Interquartile range (IQR)19

Descriptive statistics

Standard deviation17.56660673
Coefficient of variation (CV)0.2479028228
Kurtosis0.3041430708
Mean70.86085801
Median Absolute Deviation (MAD)10
Skewness-0.8334307875
Sum9495851
Variance308.585672
MonotonicityNot monotonic
2022-04-06T13:49:04.158399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8025239
 
18.7%
959030
 
6.7%
907567
 
5.6%
756649
 
4.9%
703760
 
2.8%
793361
 
2.5%
782776
 
2.1%
742640
 
2.0%
732466
 
1.8%
772294
 
1.7%
Other values (100)68225
50.5%
ValueCountFrequency (%)
41
 
< 0.1%
53
 
< 0.1%
65
 
< 0.1%
714
 
< 0.1%
812
 
< 0.1%
921
 
< 0.1%
1028
< 0.1%
1136
< 0.1%
1247
< 0.1%
1358
< 0.1%
ValueCountFrequency (%)
1421
 
< 0.1%
1371
 
< 0.1%
1281
 
< 0.1%
1241
 
< 0.1%
1111
 
< 0.1%
1101
 
< 0.1%
1081
 
< 0.1%
1061
 
< 0.1%
10538
< 0.1%
10429
< 0.1%

Number_of_Borrowers
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing31
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.587910257
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:04.275228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5084095479
Coefficient of variation (CV)0.3201752403
Kurtosis-0.5113864406
Mean1.587910257
Median Absolute Deviation (MAD)0
Skewness-0.06751714976
Sum214379
Variance0.2584802684
MonotonicityNot monotonic
2022-04-06T13:49:04.362739image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
277876
57.7%
156485
41.8%
3466
 
0.3%
4166
 
0.1%
58
 
< 0.1%
64
 
< 0.1%
82
 
< 0.1%
(Missing)31
 
< 0.1%
ValueCountFrequency (%)
156485
41.8%
277876
57.7%
3466
 
0.3%
4166
 
0.1%
58
 
< 0.1%
64
 
< 0.1%
82
 
< 0.1%
ValueCountFrequency (%)
82
 
< 0.1%
64
 
< 0.1%
58
 
< 0.1%
4166
 
0.1%
3466
 
0.3%
277876
57.7%
156485
41.8%

Original_Debt_to_Income_Ratio
Real number (ℝ≥0)

MISSING

Distinct64
Distinct (%)< 0.1%
Missing2642
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean33.29873259
Minimum1
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:04.492611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q125
median33
Q342
95-th percentile52
Maximum64
Range63
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.50869764
Coefficient of variation (CV)0.3456196902
Kurtosis-0.3792129474
Mean33.29873259
Median Absolute Deviation (MAD)8
Skewness0.08045905992
Sum4408619
Variance132.4501214
MonotonicityNot monotonic
2022-04-06T13:49:04.638314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
444168
 
3.1%
344121
 
3.1%
384098
 
3.0%
394096
 
3.0%
364082
 
3.0%
414067
 
3.0%
424049
 
3.0%
374046
 
3.0%
314017
 
3.0%
404008
 
3.0%
Other values (54)91644
67.9%
ValueCountFrequency (%)
111
 
< 0.1%
242
 
< 0.1%
350
 
< 0.1%
473
 
0.1%
5111
 
0.1%
6181
 
0.1%
7238
 
0.2%
8354
0.3%
9445
0.3%
10635
0.5%
ValueCountFrequency (%)
64359
0.3%
63368
0.3%
62342
0.3%
61423
0.3%
60448
0.3%
59490
0.4%
58514
0.4%
57543
0.4%
56696
0.5%
55741
0.5%

Borrower_Credit_Score_at_Origination
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct347
Distinct (%)0.3%
Missing557
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean742.4287966
Minimum361
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:04.792441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum361
5-th percentile641
Q1707
median755
Q3786
95-th percentile808
Maximum850
Range489
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.42807552
Coefficient of variation (CV)0.07196390517
Kurtosis0.2222909648
Mean742.4287966
Median Absolute Deviation (MAD)36
Skewness-0.8345198247
Sum99842567
Variance2854.559254
MonotonicityNot monotonic
2022-04-06T13:49:04.933304image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7911474
 
1.1%
8011468
 
1.1%
7841388
 
1.0%
7971381
 
1.0%
8021358
 
1.0%
7901357
 
1.0%
7861312
 
1.0%
7981300
 
1.0%
7881296
 
1.0%
7821288
 
1.0%
Other values (337)120859
89.5%
ValueCountFrequency (%)
3611
 
< 0.1%
4001
 
< 0.1%
4447
< 0.1%
4671
 
< 0.1%
4761
 
< 0.1%
4801
 
< 0.1%
4881
 
< 0.1%
4932
 
< 0.1%
4941
 
< 0.1%
4963
< 0.1%
ValueCountFrequency (%)
8503
 
< 0.1%
8431
 
< 0.1%
8382
 
< 0.1%
8372
 
< 0.1%
8352
 
< 0.1%
8332
 
< 0.1%
83212
< 0.1%
8312
 
< 0.1%
8302
 
< 0.1%
82918
< 0.1%

Loan_purpose
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
P
47900 
R
46740 
C
40348 
U
 
50

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP
2nd rowP
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
P47900
35.5%
R46740
34.6%
C40348
29.9%
U50
 
< 0.1%

Length

2022-04-06T13:49:05.223605image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-06T13:49:05.290664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
p47900
35.5%
r46740
34.6%
c40348
29.9%
u50
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Property_type
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
SF
99740 
PU
23060 
CO
10701 
MH
 
864
CP
 
673

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSF
2nd rowSF
3rd rowSF
4th rowSF
5th rowSF

Common Values

ValueCountFrequency (%)
SF99740
73.9%
PU23060
 
17.1%
CO10701
 
7.9%
MH864
 
0.6%
CP673
 
0.5%

Length

2022-04-06T13:49:05.364826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-06T13:49:05.444601image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
sf99740
73.9%
pu23060
 
17.1%
co10701
 
7.9%
mh864
 
0.6%
cp673
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Number_of_units
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1.0
131633 
2.0
 
2504
3.0
 
477
4.0
 
424

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0131633
97.5%
2.02504
 
1.9%
3.0477
 
0.4%
4.0424
 
0.3%

Length

2022-04-06T13:49:05.547945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-06T13:49:05.611520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0131633
97.5%
2.02504
 
1.9%
3.0477
 
0.4%
4.0424
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Occupancy_type
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
P
120399 
I
 
9238
S
 
5401

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP
2nd rowP
3rd rowP
4th rowI
5th rowP

Common Values

ValueCountFrequency (%)
P120399
89.2%
I9238
 
6.8%
S5401
 
4.0%

Length

2022-04-06T13:49:05.702691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-06T13:49:05.764355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
p120399
89.2%
i9238
 
6.8%
s5401
 
4.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Property_state
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
CA
20316 
TX
 
7990
FL
 
7327
IL
 
6468
MI
 
4909
Other values (49)
88028 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAL
2nd rowAL
3rd rowAL
4th rowAL
5th rowAR

Common Values

ValueCountFrequency (%)
CA20316
 
15.0%
TX7990
 
5.9%
FL7327
 
5.4%
IL6468
 
4.8%
MI4909
 
3.6%
NY4886
 
3.6%
WA4293
 
3.2%
PA4291
 
3.2%
MA4257
 
3.2%
NJ3982
 
2.9%
Other values (44)66319
49.1%

Length

2022-04-06T13:49:05.846740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca20316
 
15.0%
tx7990
 
5.9%
fl7327
 
5.4%
il6468
 
4.8%
mi4909
 
3.6%
ny4886
 
3.6%
wa4293
 
3.2%
pa4291
 
3.2%
ma4257
 
3.2%
nj3982
 
2.9%
Other values (44)66319
49.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Zip_code_short
Real number (ℝ≥0)

HIGH CORRELATION

Distinct892
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean545.9760956
Minimum0
Maximum999
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:05.960110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile56
Q1295
median549
Q3844
95-th percentile970
Maximum999
Range999
Interquartile range (IQR)549

Descriptive statistics

Standard deviation306.0106241
Coefficient of variation (CV)0.560483557
Kurtosis-1.301477705
Mean545.9760956
Median Absolute Deviation (MAD)268
Skewness-0.1170295055
Sum73727520
Variance93642.50206
MonotonicityNot monotonic
2022-04-06T13:49:06.091842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9451772
 
1.3%
3001194
 
0.9%
6001159
 
0.9%
7501148
 
0.9%
9261135
 
0.8%
8521099
 
0.8%
9801090
 
0.8%
9171070
 
0.8%
606958
 
0.7%
481957
 
0.7%
Other values (882)123456
91.4%
ValueCountFrequency (%)
03
 
< 0.1%
695
0.1%
7117
0.1%
815
 
< 0.1%
9165
0.1%
10152
0.1%
1147
 
< 0.1%
1254
 
< 0.1%
1321
 
< 0.1%
14124
0.1%
ValueCountFrequency (%)
9994
 
< 0.1%
99823
 
< 0.1%
99735
 
< 0.1%
99669
 
0.1%
995139
0.1%
99410
 
< 0.1%
993165
0.1%
992188
0.1%
99141
 
< 0.1%
99089
0.1%

Primary_mortgage_insurance_percent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct20
Distinct (%)0.1%
Missing110716
Missing (%)82.0%
Infinite0
Infinite (%)0.0%
Mean23.75980594
Minimum6
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:06.222829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile12
Q118
median25
Q330
95-th percentile30
Maximum50
Range44
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.15112836
Coefficient of variation (CV)0.3009758741
Kurtosis-0.3705245698
Mean23.75980594
Median Absolute Deviation (MAD)5
Skewness-0.7907650624
Sum577886
Variance51.13863683
MonotonicityNot monotonic
2022-04-06T13:49:06.316823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
259396
 
7.0%
307489
 
5.5%
123806
 
2.8%
171334
 
1.0%
35953
 
0.7%
6625
 
0.5%
18474
 
0.4%
16210
 
0.2%
2010
 
< 0.1%
407
 
< 0.1%
Other values (10)18
 
< 0.1%
(Missing)110716
82.0%
ValueCountFrequency (%)
6625
 
0.5%
101
 
< 0.1%
123806
2.8%
154
 
< 0.1%
16210
 
0.2%
171334
 
1.0%
18474
 
0.4%
2010
 
< 0.1%
221
 
< 0.1%
231
 
< 0.1%
ValueCountFrequency (%)
502
 
< 0.1%
407
 
< 0.1%
391
 
< 0.1%
381
 
< 0.1%
35953
 
0.7%
331
 
< 0.1%
307489
5.5%
282
 
< 0.1%
274
 
< 0.1%
259396
7.0%

Product_type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
FRM
135038 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFRM
2nd rowFRM
3rd rowFRM
4th rowFRM
5th rowFRM

Common Values

ValueCountFrequency (%)
FRM135038
100.0%

Length

2022-04-06T13:49:06.419829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-06T13:49:06.482267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
frm135038
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Co-borrower_credit_score_at_origination
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct327
Distinct (%)0.5%
Missing67672
Missing (%)50.1%
Infinite0
Infinite (%)0.0%
Mean751.1459045
Minimum400
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:06.575392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile651
Q1721
median765
Q3791
95-th percentile810
Maximum850
Range450
Interquartile range (IQR)70

Descriptive statistics

Standard deviation50.57622641
Coefficient of variation (CV)0.06733209369
Kurtosis0.6414205765
Mean751.1459045
Median Absolute Deviation (MAD)31
Skewness-1.005586451
Sum50601695
Variance2557.954678
MonotonicityNot monotonic
2022-04-06T13:49:06.716824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
801878
 
0.7%
791870
 
0.6%
790837
 
0.6%
798805
 
0.6%
796798
 
0.6%
793798
 
0.6%
797789
 
0.6%
784778
 
0.6%
802771
 
0.6%
809755
 
0.6%
Other values (317)59287
43.9%
(Missing)67672
50.1%
ValueCountFrequency (%)
4001
< 0.1%
4671
< 0.1%
4711
< 0.1%
4841
< 0.1%
4861
< 0.1%
4871
< 0.1%
4981
< 0.1%
5011
< 0.1%
5052
< 0.1%
5122
< 0.1%
ValueCountFrequency (%)
8501
 
< 0.1%
8392
 
< 0.1%
8382
 
< 0.1%
8351
 
< 0.1%
8342
 
< 0.1%
8331
 
< 0.1%
8323
 
< 0.1%
8311
 
< 0.1%
8303
 
< 0.1%
8299
< 0.1%

Mortgage_Insurance_type
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing110716
Missing (%)82.0%
Memory size1.0 MiB
1.0
22332 
2.0
 
1988
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.022332
 
16.5%
2.01988
 
1.5%
3.02
 
< 0.1%
(Missing)110716
82.0%

Length

2022-04-06T13:49:06.853975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-06T13:49:06.924160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.022332
91.8%
2.01988
 
8.2%
3.02
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Origination_Date
Categorical

HIGH CARDINALITY

Distinct234
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2003-07-01
 
2163
2003-04-01
 
2128
2003-06-01
 
1985
2003-05-01
 
1840
2002-12-01
 
1775
Other values (229)
125147 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2007-02-01
2nd row2007-02-01
3rd row2007-02-01
4th row2007-02-01
5th row2007-02-01

Common Values

ValueCountFrequency (%)
2003-07-012163
 
1.6%
2003-04-012128
 
1.6%
2003-06-011985
 
1.5%
2003-05-011840
 
1.4%
2002-12-011775
 
1.3%
2002-10-011770
 
1.3%
2003-03-011727
 
1.3%
2002-11-011637
 
1.2%
2003-08-011629
 
1.2%
2003-01-011545
 
1.1%
Other values (224)116839
86.5%

Length

2022-04-06T13:49:06.997617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2003-07-012163
 
1.6%
2003-04-012128
 
1.6%
2003-06-011985
 
1.5%
2003-05-011840
 
1.4%
2002-12-011775
 
1.3%
2002-10-011770
 
1.3%
2003-03-011727
 
1.3%
2002-11-011637
 
1.2%
2003-08-011629
 
1.2%
2003-01-011545
 
1.1%
Other values (224)116839
86.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

First_payment_date
Categorical

HIGH CARDINALITY

Distinct235
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2003-09-01
 
2144
2003-06-01
 
2109
2003-08-01
 
1993
2003-07-01
 
1851
2003-05-01
 
1785
Other values (230)
125156 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2007-03-01
2nd row2007-04-01
3rd row2007-04-01
4th row2007-04-01
5th row2007-04-01

Common Values

ValueCountFrequency (%)
2003-09-012144
 
1.6%
2003-06-012109
 
1.6%
2003-08-011993
 
1.5%
2003-07-011851
 
1.4%
2003-05-011785
 
1.3%
2002-12-011740
 
1.3%
2003-02-011735
 
1.3%
2003-01-011649
 
1.2%
2003-10-011595
 
1.2%
2003-03-011550
 
1.1%
Other values (225)116887
86.6%

Length

2022-04-06T13:49:07.098755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2003-09-012144
 
1.6%
2003-06-012109
 
1.6%
2003-08-011993
 
1.5%
2003-07-011851
 
1.4%
2003-05-011785
 
1.3%
2002-12-011740
 
1.3%
2003-02-011735
 
1.3%
2003-01-011649
 
1.2%
2003-10-011595
 
1.2%
2003-03-011550
 
1.1%
Other values (225)116887
86.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

First_time_home_buyer_indicator
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
N
120561 
Y
14409 
U
 
68

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N120561
89.3%
Y14409
 
10.7%
U68
 
0.1%

Length

2022-04-06T13:49:07.194093image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-06T13:49:07.256548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
n120561
89.3%
y14409
 
10.7%
u68
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

UNRATE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.158662006
Minimum3.8
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:07.348448image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile4.1
Q14.9
median5.7
Q37.5
95-th percentile9.5
Maximum10
Range6.2
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation1.657191534
Coefficient of variation (CV)0.2690830464
Kurtosis-0.4580471642
Mean6.158662006
Median Absolute Deviation (MAD)0.9
Skewness0.7974035707
Sum831653.4
Variance2.746283782
MonotonicityNot monotonic
2022-04-06T13:49:07.490946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.79928
 
7.4%
58106
 
6.0%
6.16034
 
4.5%
5.95976
 
4.4%
5.85757
 
4.3%
4.95389
 
4.0%
4.74805
 
3.6%
64702
 
3.5%
4.44485
 
3.3%
5.63884
 
2.9%
Other values (45)75972
56.3%
ValueCountFrequency (%)
3.8634
 
0.5%
3.91712
 
1.3%
42930
2.2%
4.12837
2.1%
4.22392
1.8%
4.33223
2.4%
4.44485
3.3%
4.52153
1.6%
4.62821
2.1%
4.74805
3.6%
ValueCountFrequency (%)
10444
 
0.3%
9.91743
1.3%
9.81818
1.3%
9.6780
 
0.6%
9.52914
2.2%
9.42724
2.0%
9.3723
 
0.5%
9.11108
 
0.8%
93408
2.5%
8.8712
 
0.5%

CPIAUCSL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct223
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208.1883261
Minimum164.7
Maximum251.176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:07.778541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum164.7
5-th percentile176.1
Q1183.1
median212.495
Q3231.797
95-th percentile244.028
Maximum251.176
Range86.476
Interquartile range (IQR)48.697

Descriptive statistics

Standard deviation24.77699426
Coefficient of variation (CV)0.1190124092
Kurtosis-1.534392425
Mean208.1883261
Median Absolute Deviation (MAD)25.095
Skewness0.03714129161
Sum28113335.18
Variance613.8994448
MonotonicityNot monotonic
2022-04-06T13:49:07.921899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177.42905
 
2.2%
183.72163
 
1.6%
183.22128
 
1.6%
183.11985
 
1.5%
177.71905
 
1.4%
182.91840
 
1.4%
181.81775
 
1.3%
181.21770
 
1.3%
183.91727
 
1.3%
181.51637
 
1.2%
Other values (213)115203
85.3%
ValueCountFrequency (%)
164.720
 
< 0.1%
164.86
 
< 0.1%
165.96
 
< 0.1%
16618
 
< 0.1%
166.77
 
< 0.1%
167.112
 
< 0.1%
167.814
 
< 0.1%
168.127
 
< 0.1%
168.4135
0.1%
168.8292
0.2%
ValueCountFrequency (%)
251.176118
 
0.1%
250.64342
0.3%
249.957431
0.3%
249.475384
0.3%
249.413397
0.3%
248.816459
0.3%
247.847567
0.4%
247.333544
0.4%
246.57594
0.4%
246.445552
0.4%

Qdate
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2003-04-01
 
5953
2002-10-01
 
5182
2003-07-01
 
4791
2003-01-01
 
4754
2001-10-01
 
3783
Other values (73)
110575 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2007-01-01
2nd row2007-01-01
3rd row2007-01-01
4th row2007-01-01
5th row2007-01-01

Common Values

ValueCountFrequency (%)
2003-04-015953
 
4.4%
2002-10-015182
 
3.8%
2003-07-014791
 
3.5%
2003-01-014754
 
3.5%
2001-10-013783
 
2.8%
2002-07-013299
 
2.4%
2001-04-012922
 
2.2%
2009-04-012666
 
2.0%
2002-01-012577
 
1.9%
2012-10-012572
 
1.9%
Other values (68)96539
71.5%

Length

2022-04-06T13:49:08.071668image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2003-04-015953
 
4.4%
2002-10-015182
 
3.8%
2003-07-014791
 
3.5%
2003-01-014754
 
3.5%
2001-10-013783
 
2.8%
2002-07-013299
 
2.4%
2001-04-012922
 
2.2%
2009-04-012666
 
2.0%
2002-01-012577
 
1.9%
2012-10-012572
 
1.9%
Other values (68)96539
71.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rGDP
Real number (ℝ)

HIGH CORRELATION

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.083868244
Minimum-8.4
Maximum7.5
Zeros0
Zeros (%)0.0%
Negative17017
Negative (%)12.6%
Memory size1.0 MiB
2022-04-06T13:49:08.202068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-8.4
5-th percentile-1.7
Q10.9
median2.2
Q33.5
95-th percentile5.5
Maximum7.5
Range15.9
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation2.31057909
Coefficient of variation (CV)1.108793273
Kurtosis3.309587534
Mean2.083868244
Median Absolute Deviation (MAD)1.3
Skewness-0.8653541067
Sum281401.4
Variance5.338775732
MonotonicityNot monotonic
2022-04-06T13:49:08.340163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3.512026
 
8.9%
2.211496
 
8.5%
0.58479
 
6.3%
3.28333
 
6.2%
0.66134
 
4.5%
25702
 
4.2%
75245
 
3.9%
2.44933
 
3.7%
2.33969
 
2.9%
4.73902
 
2.9%
Other values (33)64819
48.0%
ValueCountFrequency (%)
-8.41073
 
0.8%
-4.42555
1.9%
-2.31452
1.1%
-2.11036
 
0.8%
-1.72388
1.8%
-1.13157
2.3%
-11218
 
0.9%
-0.62666
2.0%
-0.11472
1.1%
0.11404
1.0%
ValueCountFrequency (%)
7.51072
 
0.8%
75245
3.9%
5.51211
 
0.9%
5.4839
 
0.6%
5.333
 
< 0.1%
51351
 
1.0%
4.73902
2.9%
4.52402
1.8%
4.11245
 
0.9%
3.81224
 
0.9%

TCMR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct233
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.478040072
Minimum1.504
Maximum6.661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:08.498232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.504
5-th percentile1.719
Q12.324545455
median3.675
Q34.347619048
95-th percentile5.284
Maximum6.661
Range5.157
Interquartile range (IQR)2.023073593

Descriptive statistics

Standard deviation1.207100069
Coefficient of variation (CV)0.3470633012
Kurtosis-0.9648517968
Mean3.478040072
Median Absolute Deviation (MAD)1.0405
Skewness0.1287623962
Sum469667.5753
Variance1.457090577
MonotonicityNot monotonic
2022-04-06T13:49:08.654777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.9754545452163
 
1.6%
3.9585714292128
 
1.6%
3.3342857141985
 
1.5%
3.5690476191840
 
1.4%
4.0323809521775
 
1.3%
3.9409090911770
 
1.3%
3.8071428571727
 
1.3%
4.0484210531637
 
1.2%
4.4452380951629
 
1.2%
5.08751582
 
1.2%
Other values (223)116802
86.5%
ValueCountFrequency (%)
1.504628
0.5%
1.526666667788
0.6%
1.556521739880
0.7%
1.622380952771
0.6%
1.63047619846
0.6%
1.644090909662
0.5%
1.654867
0.6%
1.67826087891
0.7%
1.719825
0.6%
1.723157895767
0.6%
ValueCountFrequency (%)
6.661231
0.2%
6.5195268
0.2%
6.440454545370
0.3%
6.275454545292
0.2%
6.256521739332
0.2%
6.11227
 
< 0.1%
6.097272727410
0.3%
6.054349
0.3%
6.034135
 
0.1%
5.990526316292
0.2%

POILWTIUSDM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct233
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.27779559
Minimum11.99
Maximum133.9271429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:08.792828image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum11.99
5-th percentile25.51
Q130.71
median48.74863636
Q381.89952381
95-th percentile103.2842857
Maximum133.9271429
Range121.9371429
Interquartile range (IQR)51.18952381

Descriptive statistics

Standard deviation27.84104655
Coefficient of variation (CV)0.494707482
Kurtosis-0.9150173174
Mean56.27779559
Median Absolute Deviation (MAD)20.34863636
Skewness0.5551308717
Sum7599640.961
Variance775.123873
MonotonicityNot monotonic
2022-04-06T13:49:08.950235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.734347832163
 
1.6%
28.328181822128
 
1.6%
30.711985
 
1.5%
28.201818181840
 
1.4%
29.441775
 
1.3%
28.851770
 
1.3%
33.315714291727
 
1.3%
26.561637
 
1.2%
31.583809521629
 
1.2%
32.915217391545
 
1.1%
Other values (223)116839
86.5%
ValueCountFrequency (%)
11.9910
 
< 0.1%
12.3410
 
< 0.1%
14.366
 
< 0.1%
17.236
 
< 0.1%
17.7212
 
< 0.1%
17.756
 
< 0.1%
19.311281
0.9%
19.591318
1.0%
19.69964
0.7%
19.897
 
< 0.1%
ValueCountFrequency (%)
133.9271429453
0.3%
133.8956522358
0.3%
125.6554545473
0.4%
116.6404762361
0.3%
112.6186364599
0.4%
110.0428571308
0.2%
106.5463636625
0.5%
106.3138095460
0.3%
106.1504545731
0.5%
105.3447619476
0.4%

TTLCONS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct234
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean963119.3409
Minimum708818
Maximum1335425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:09.096037image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum708818
5-th percentile798248
Q1846777
median891264
Q31101187
95-th percentile1260760
Maximum1335425
Range626607
Interquartile range (IQR)254410

Descriptive statistics

Standard deviation153846.4496
Coefficient of variation (CV)0.1597376805
Kurtosis-0.7613237989
Mean963119.3409
Median Absolute Deviation (MAD)73530
Skewness0.7804033785
Sum1.300577096 × 1011
Variance2.366873004 × 1010
MonotonicityNot monotonic
2022-04-06T13:49:09.246694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8912642163
 
1.6%
8594592128
 
1.6%
8808651985
 
1.5%
8668141840
 
1.4%
8559211775
 
1.3%
8396901770
 
1.3%
8511321727
 
1.3%
8446971637
 
1.2%
9018391629
 
1.2%
8638551545
 
1.1%
Other values (224)116839
86.5%
ValueCountFrequency (%)
70881810
 
< 0.1%
72630810
 
< 0.1%
7273586
 
< 0.1%
7287886
 
< 0.1%
7372266
 
< 0.1%
73916712
 
< 0.1%
74152412
 
< 0.1%
7458157
 
< 0.1%
75140214
 
< 0.1%
755410379
0.3%
ValueCountFrequency (%)
1335425384
0.3%
1333468342
0.3%
1322364431
0.3%
1317790118
 
0.1%
1312855397
0.3%
1297449459
0.3%
1295930567
0.4%
1290213544
0.4%
1269670387
0.3%
1268793512
0.4%

DEXUSEU
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct234
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.180771125
Minimum0.8525380952
Maximum1.575863636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:09.397994image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.8525380952
5-th percentile0.8859954545
Q11.072657895
median1.191335
Q31.316019048
95-th percentile1.440286364
Maximum1.575863636
Range0.7233255411
Interquartile range (IQR)0.2433611529

Descriptive statistics

Standard deviation0.1727537297
Coefficient of variation (CV)0.1463058556
Kurtosis-0.7560880363
Mean1.180771125
Median Absolute Deviation (MAD)0.1222306522
Skewness-0.15430538
Sum159448.9711
Variance0.02984385113
MonotonicityNot monotonic
2022-04-06T13:49:09.534032image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1365045452163
 
1.6%
1.0862363642128
 
1.6%
1.1674285711985
 
1.5%
1.1555571431840
 
1.4%
1.0194047621775
 
1.3%
0.981151770
 
1.3%
1.0797428571727
 
1.3%
1.0012894741637
 
1.2%
1.1155190481629
 
1.2%
1.0622476191545
 
1.1%
Other values (224)116839
86.5%
ValueCountFrequency (%)
0.8525380952442
0.3%
0.8529571429941
0.7%
0.855152381437
0.3%
0.8614857143745
0.6%
0.869475390
 
0.3%
0.8706947368779
0.6%
0.87531363641040
0.8%
0.8766380952834
0.6%
0.8831619048964
0.7%
0.8859954545687
0.5%
ValueCountFrequency (%)
1.575863636358
0.3%
1.575363636599
0.4%
1.556171429453
0.3%
1.555414286473
0.4%
1.552019048476
0.4%
1.495538095361
0.3%
1.490752632428
0.3%
1.482114286444
0.3%
1.475945572
0.4%
1.472814286404
0.3%

BOPGSTB
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct231
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-42336.57545
Minimum-67823
Maximum-15946
Zeros0
Zeros (%)0.0%
Negative135038
Negative (%)100.0%
Memory size1.0 MiB
2022-04-06T13:49:09.678821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-67823
5-th percentile-61797
Q1-45943
median-41360
Q3-36519
95-th percentile-28800
Maximum-15946
Range51877
Interquartile range (IQR)9424

Descriptive statistics

Standard deviation9404.518716
Coefficient of variation (CV)-0.2221369729
Kurtosis0.1517299755
Mean-42336.57545
Median Absolute Deviation (MAD)4841
Skewness-0.6459291409
Sum-5717046476
Variance88444972.27
MonotonicityNot monotonic
2022-04-06T13:49:09.831199image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-413632163
 
1.6%
-419872128
 
1.6%
-396211985
 
1.5%
-407791840
 
1.4%
-432941775
 
1.3%
-351711770
 
1.3%
-433551727
 
1.3%
-396241637
 
1.2%
-398021629
 
1.2%
-378421608
 
1.2%
Other values (221)116776
86.5%
ValueCountFrequency (%)
-67823332
0.2%
-67140441
0.3%
-66842358
0.3%
-66680231
0.2%
-66525301
0.2%
-64936319
0.2%
-64923456
0.3%
-64844337
0.2%
-64366286
0.2%
-64348572
0.4%
ValueCountFrequency (%)
-1594610
 
< 0.1%
-179006
 
< 0.1%
-182546
 
< 0.1%
-1878610
 
< 0.1%
-202296
 
< 0.1%
-2289914
< 0.1%
-2297712
< 0.1%
-2311512
< 0.1%
-234217
 
< 0.1%
-2353127
< 0.1%

GOLDAMGBD228NLBM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct234
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean845.9498032
Minimum256.1977273
Maximum1780.647727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:09.971753image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum256.1977273
5-th percentile272.0571429
Q1350.7652174
median857.7261905
Q31273.579545
95-th percentile1671.886364
Maximum1780.647727
Range1524.45
Interquartile range (IQR)922.8143281

Descriptive statistics

Standard deviation498.1153598
Coefficient of variation (CV)0.5888237788
Kurtosis-1.438866177
Mean845.9498032
Median Absolute Deviation (MAD)479.702381
Skewness0.2444715983
Sum114235369.5
Variance248118.9117
MonotonicityNot monotonic
2022-04-06T13:49:10.111899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350.76521742163
 
1.6%
328.20752128
 
1.6%
356.91190481985
 
1.5%
355.4051840
 
1.4%
333.31775
 
1.3%
316.74782611770
 
1.3%
341.56428571727
 
1.3%
319.25476191637
 
1.2%
358.99251629
 
1.2%
356.86363641545
 
1.1%
Other values (224)116839
86.5%
ValueCountFrequency (%)
256.19772737
 
< 0.1%
256.935714312
 
< 0.1%
260.75941
0.7%
261.402272712
 
< 0.1%
262.0175759
0.6%
263.2727273990
0.7%
264.470454514
 
< 0.1%
265.9340909499
0.4%
265.9886364437
0.3%
267.7068182745
0.6%
ValueCountFrequency (%)
1780.647727664
0.5%
1759.5454
0.3%
1746.347826880
0.7%
1743.095238669
0.5%
1741.925767
0.6%
1735.977273688
0.5%
1724.352273867
0.6%
1687.342105825
0.6%
1675.056818731
0.5%
1671.886364716
0.5%

CSUSHPISA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct234
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.6342832
Minimum93.236
Maximum202.411
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:10.410436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum93.236
5-th percentile111.111
Q1130.151
median145.632
Q3169.868
95-th percentile188.951
Maximum202.411
Range109.175
Interquartile range (IQR)39.717

Descriptive statistics

Standard deviation24.71270765
Coefficient of variation (CV)0.1662651922
Kurtosis-0.887312779
Mean148.6342832
Median Absolute Deviation (MAD)18.965
Skewness0.1780414115
Sum20071276.33
Variance610.7179195
MonotonicityNot monotonic
2022-04-06T13:49:10.551818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133.7772163
 
1.6%
130.8862128
 
1.6%
132.6511985
 
1.5%
131.7371840
 
1.4%
127.6191775
 
1.3%
125.7331770
 
1.3%
130.1511727
 
1.3%
126.6671637
 
1.2%
134.9691629
 
1.2%
128.4621545
 
1.1%
Other values (224)116839
86.5%
ValueCountFrequency (%)
93.23610
 
< 0.1%
93.69810
 
< 0.1%
94.2446
 
< 0.1%
94.8136
 
< 0.1%
95.3726
 
< 0.1%
96.00312
< 0.1%
96.6197
 
< 0.1%
97.24612
< 0.1%
97.89214
< 0.1%
98.55427
< 0.1%
ValueCountFrequency (%)
202.411118
 
0.1%
201.758342
0.3%
201.091431
0.3%
200.347397
0.3%
199.461384
0.3%
198.472459
0.3%
197.201567
0.4%
196.012544
0.4%
194.88594
0.4%
193.794552
0.4%

MSPUS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct76
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231137.665
Minimum157400
Maximum337900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2022-04-06T13:49:10.691980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum157400
5-th percentile171100
Q1190100
median224100
Q3258400
95-th percentile315600
Maximum337900
Range180500
Interquartile range (IQR)68300

Descriptive statistics

Standard deviation46186.08414
Coefficient of variation (CV)0.1998206746
Kurtosis-0.7330918917
Mean231137.665
Median Absolute Deviation (MAD)34000
Skewness0.5377179553
Sum3.1212368 × 1010
Variance2133154368
MonotonicityNot monotonic
2022-04-06T13:49:10.823410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1918005953
 
4.4%
1901005182
 
3.8%
1919004791
 
3.5%
1860004754
 
3.5%
1711003783
 
2.8%
1781003299
 
2.4%
2384003286
 
2.4%
1790002922
 
2.2%
2209002666
 
2.0%
1887002577
 
1.9%
Other values (66)95825
71.0%
ValueCountFrequency (%)
15740026
 
< 0.1%
15870024
 
< 0.1%
15910033
 
< 0.1%
1632001072
 
0.8%
1653001285
 
1.0%
1688001142
 
0.8%
1698002248
1.7%
1711003783
2.8%
1725002388
1.8%
1729001322
 
1.0%
ValueCountFrequency (%)
3379001705
1.3%
3318001240
0.9%
3205001723
1.3%
3182001685
1.2%
315600891
 
0.7%
3131001392
1.0%
3109002100
1.6%
3060001948
1.4%
3038002354
1.7%
3025001404
1.0%

Interactions

2022-04-06T13:48:54.492718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:28.139162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:31.952461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:36.003426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:39.824251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:43.569273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:47.640564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:52.853398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:57.153701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:01.435036image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:05.783250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:09.574683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:12.478643image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:15.858492image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:19.724519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:23.402012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:27.238914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:30.998414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:35.000314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:38.961019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:42.858895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:46.822774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:50.739434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:54.683894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:28.317138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:32.120455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:36.181483image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:39.972846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:43.722039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:47.815597image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:53.026162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:57.353168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:01.611837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:05.946945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:09.697944image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:12.611610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:16.013467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:19.876085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:23.569793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:27.548813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:31.159860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:35.164556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:39.112907image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:43.021372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:46.983508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:50.905096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:54.864316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:28.479086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:32.304915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:36.350454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:40.116241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:43.907399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:47.979220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:53.212815image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:57.553981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:01.785053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:06.102533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:09.818927image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:12.774240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:16.171853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:20.035994image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:23.729536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:27.691318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:31.313420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:35.331870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:39.276834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:43.176582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:47.140765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:51.059901image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:55.049313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:28.636096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:32.468406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:36.513928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:40.273481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:44.173150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:48.173195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:53.415191image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:57.744124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:01.966598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:06.281910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:09.951493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:12.921358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:16.341298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:20.197264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:23.902149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:27.842714image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:31.505964image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:35.517839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:39.443184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:43.358217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:47.312253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:51.213589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:55.230490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:28.790993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2022-04-06T13:48:05.419313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:09.142838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:12.201252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:15.552294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:19.383152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:23.091764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:26.879450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:30.674179image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:34.658319image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:38.606805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:42.518857image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:46.335872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:50.401061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:54.163713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:58.431920image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:31.752127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:35.830253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:39.668811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:43.408710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:47.323617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:52.653718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:47:56.959326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:01.241951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:05.606460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:09.442884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:12.332856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:15.691063image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:19.548485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:23.235634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:27.036593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:30.833550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:34.817527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:38.763590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:42.690868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:46.490104image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:50.563814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-04-06T13:48:54.320726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-04-06T13:49:10.995784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-06T13:49:11.335493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-06T13:49:11.665463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-06T13:49:11.982435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-06T13:49:12.233358image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-06T13:48:58.823280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-06T13:49:00.281336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-06T13:49:01.182196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-06T13:49:01.581069image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Loan_IdentifierOrigination_ChannelSeller_NameOriginal_Interest_RateOriginal_UPBOriginal_Loan_TermOriginal_LTV_(OLTV)Original_Combined_LTV_(CLTV)Number_of_BorrowersOriginal_Debt_to_Income_RatioBorrower_Credit_Score_at_OriginationLoan_purposeProperty_typeNumber_of_unitsOccupancy_typeProperty_stateZip_code_shortPrimary_mortgage_insurance_percentProduct_typeCo-borrower_credit_score_at_originationMortgage_Insurance_typeOrigination_DateFirst_payment_dateFirst_time_home_buyer_indicatorUNRATECPIAUCSLQdaterGDPTCMRPOILWTIUSDMTTLCONSDEXUSEUBOPGSTBGOLDAMGBD228NLBMCSUSHPISAMSPUS
09.733730e+11BOTHER6.87532000.0360.090.090.01.022.0669.0PSF1.0PAL358.035.0FRMNaN1.02007-02-012007-03-01Y4.5204.2262007-01-010.94.72263259.2571138752.01.308021-58478.0665.1025184.601257400.0
19.276200e+11BPNC BANK, N.A.5.875200000.0360.080.080.02.026.0693.0PSF1.0PAL350.0NaNFRM675.0NaN2007-02-012007-04-01N4.5204.2262007-01-010.94.72263259.2571138752.01.308021-58478.0665.1025184.601257400.0
27.176670e+11BOTHER6.250122000.0180.080.080.02.031.0741.0CSF1.0PAL359.0NaNFRM759.0NaN2007-02-012007-04-01N4.5204.2262007-01-010.94.72263259.2571138752.01.308021-58478.0665.1025184.601257400.0
39.889510e+11CAMTRUST BANK6.00067000.0180.077.077.02.017.0804.0CSF1.0IAL356.0NaNFRM805.0NaN2007-02-012007-04-01N4.5204.2262007-01-010.94.72263259.2571138752.01.308021-58478.0665.1025184.601257400.0
41.908850e+11ROTHER5.87550000.0180.041.041.02.010.0658.0CSF1.0PAR728.0NaNFRM697.0NaN2007-02-012007-04-01N4.5204.2262007-01-010.94.72263259.2571138752.01.308021-58478.0665.1025184.601257400.0
57.533710e+11ROTHER6.375160000.0360.095.095.03.028.0665.0PSF1.0SAR719.030.0FRMNaN1.02007-02-012007-04-01N4.5204.2262007-01-010.94.72263259.2571138752.01.308021-58478.0665.1025184.601257400.0
67.038110e+11CCITIMORTGAGE, INC.5.875176000.0180.080.090.02.022.0773.0CSF1.0PAR721.0NaNFRMNaNNaN2007-02-012007-04-01N4.5204.2262007-01-010.94.72263259.2571138752.01.308021-58478.0665.1025184.601257400.0
77.947140e+11BFIRST TENNESSEE BANK NATIONAL ASSOCIATION7.000294000.0360.080.080.02.028.0783.0PPU1.0IAZ857.0NaNFRM782.0NaN2007-02-012007-04-01N4.5204.2262007-01-010.94.72263259.2571138752.01.308021-58478.0665.1025184.601257400.0
85.789070e+11CBANK OF AMERICA, N.A.6.375128000.0360.066.066.01.041.0668.0CSF1.0PAZ850.0NaNFRMNaNNaN2007-02-012007-04-01N4.5204.2262007-01-010.94.72263259.2571138752.01.308021-58478.0665.1025184.601257400.0
98.264440e+11CBANK OF AMERICA, N.A.6.375115000.0360.035.035.02.020.0792.0CSF1.0PAZ856.0NaNFRM704.0NaN2007-02-012007-04-01N4.5204.2262007-01-010.94.72263259.2571138752.01.308021-58478.0665.1025184.601257400.0

Last rows

Loan_IdentifierOrigination_ChannelSeller_NameOriginal_Interest_RateOriginal_UPBOriginal_Loan_TermOriginal_LTV_(OLTV)Original_Combined_LTV_(CLTV)Number_of_BorrowersOriginal_Debt_to_Income_RatioBorrower_Credit_Score_at_OriginationLoan_purposeProperty_typeNumber_of_unitsOccupancy_typeProperty_stateZip_code_shortPrimary_mortgage_insurance_percentProduct_typeCo-borrower_credit_score_at_originationMortgage_Insurance_typeOrigination_DateFirst_payment_dateFirst_time_home_buyer_indicatorUNRATECPIAUCSLQdaterGDPTCMRPOILWTIUSDMTTLCONSDEXUSEUBOPGSTBGOLDAMGBD228NLBMCSUSHPISAMSPUS
1350283.093700e+11RLOANDEPOT.COM, LLC4.875240000.0360.060.060.01.047.0711.0CPU1.0PVA201.0NaNFRMNaNNaN2018-06-012018-08-01N4.0251.1762018-04-013.52.8967.5228571317790.01.1679-47431.01299.15202.411315600.0
1350297.464840e+11ROTHER4.625415000.0360.058.058.02.027.0799.0PSF1.0PVA222.0NaNFRM786.0NaN2018-06-012018-08-01Y4.0251.1762018-04-013.52.8967.5228571317790.01.1679-47431.01299.15202.411315600.0
1350309.961980e+11ROTHER4.625118000.0180.067.067.01.028.0802.0PCO1.0SVT57.0NaNFRMNaNNaN2018-06-012018-08-01N4.0251.1762018-04-013.52.8967.5228571317790.01.1679-47431.01299.15202.411315600.0
1350311.149900e+11ROTHER4.875344000.0360.097.097.02.042.0711.0PCO1.0PWA982.025.0FRM729.01.02018-06-012018-08-01Y4.0251.1762018-04-013.52.8967.5228571317790.01.1679-47431.01299.15202.411315600.0
1350329.415520e+11RFAIRWAY INDEPENDENT MORTGAGE CORPORATION4.990364000.0360.080.080.01.045.0785.0PSF1.0PWA985.0NaNFRMNaNNaN2018-06-012018-08-01N4.0251.1762018-04-013.52.8967.5228571317790.01.1679-47431.01299.15202.411315600.0
1350339.204900e+11ROTHER4.625200000.0240.050.050.01.045.0717.0RPU1.0PWA986.0NaNFRMNaNNaN2018-06-012018-08-01N4.0251.1762018-04-013.52.8967.5228571317790.01.1679-47431.01299.15202.411315600.0
1350349.666890e+11ROTHER4.62594000.0360.047.047.01.039.0677.0RSF1.0PWI531.0NaNFRMNaNNaN2018-06-012018-08-01N4.0251.1762018-04-013.52.8967.5228571317790.01.1679-47431.01299.15202.411315600.0
1350356.616280e+11ROTHER4.625239000.0360.074.074.02.020.0788.0RSF1.0SWI545.0NaNFRM739.0NaN2018-06-012018-08-01N4.0251.1762018-04-013.52.8967.5228571317790.01.1679-47431.01299.15202.411315600.0
1350365.102850e+11ROTHER5.00093000.0360.044.044.01.019.0621.0CSF1.0PWI537.0NaNFRMNaNNaN2018-06-012018-08-01N4.0251.1762018-04-013.52.8967.5228571317790.01.1679-47431.01299.15202.411315600.0
1350374.330130e+11ROTHER5.125140000.0360.094.094.01.036.0660.0PSF1.0PWI549.030.0FRMNaN1.02018-06-012018-08-01Y4.0251.1762018-04-013.52.8967.5228571317790.01.1679-47431.01299.15202.411315600.0